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Related Questions
- Can feature importance analysis reveal biases in the recommendation algorithm by highlighting the most relevant factors that influence product suggestions?
- How do partial dependence plots help identify interactions between features and the target variable, potentially uncovering biases in the recommendation model?
- In what ways can feature importance and partial dependence plots be used to diagnose and mitigate fairness issues in e-commerce recommendation systems?
- Are there any specific metrics or techniques that can be used in conjunction with feature importance and partial dependence plots to evaluate the fairness of product recommendations?
- Can feature importance and partial dependence plots be used to identify the impact of feature engineering on the fairness of the recommendation model?
- How can these visualization techniques be applied to understand the influence of demographic or sensitive attributes on product recommendations?
- What are the limitations of relying solely on feature importance and partial dependence plots for evaluating the fairness of product recommendations in an e-commerce system?
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